# In[2]:
from Remove_link import remove_link
from Remove_number import remove_number
from Remove_punctuation import remove_punctuation
from Remove_stopwords import remove_stopwords
from Replace_netword import replace_netword
from Replace_repeatwords import replace_repeatwords
from Replace_ywz import replace_ywz
from Translate_eng import translate_eng
import time
for i in range(2000):
data_all[i]=translate_eng(data_all[i])
data_all[i]=replace_ywz(data_all[i])
data_all[i]=replace_repeatwords(data_all[i])
data_all[i]=replace_netword(data_all[i])
#data_all[i]=remove_stopwords(data_all[i])
data_all[i]=remove_punctuation(data_all[i])
data_all[i]=remove_number(data_all[i])
data_all[i]=remove_link(data_all[i])
print(i)
# In[3]:
import jieba
all_data=[]
for i in range(2000):
# coding: utf-8
# In[1]:
for i in range(100):
fname = "C:\\LAWHCA\\chinese-sentiment--analysis-preprocess\\neg\\neg." + str(
i) + ".txt"
print(fname)
with open(fname, "r", errors="ignore") as f:
z = f.read()
from Translate_eng import translate_eng
print(translate_eng(z))
# In[2]:
# In[1]:
neg = []
for i in range(1000):
fname = "C:\\LAWHCA\\chinese-sentiment--analysis-preprocess\\neg\\neg." + str(
i) + ".txt"
print(fname)
with open(fname, "r", errors="ignore") as f:
neg.append(f.read())
# In[2]:
pos = []
for i in range(1000):
fname = "C:\\LAWHCA\\chinese-sentiment--analysis-preprocess\\pos\\pos." + str(
i) + ".txt"